Xin Song

95 total papers · 2.2k total citations
57 papers, 879 citations indexed

About

Xin Song is a scholar working on Molecular Biology, Surgery and Oncology. According to data from OpenAlex, Xin Song has authored 57 papers receiving a total of 879 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 19 papers in Surgery and 19 papers in Oncology. Recurrent topics in Xin Song's work include Esophageal Cancer Research and Treatment (16 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and Epigenetics and DNA Methylation (6 papers). Xin Song is often cited by papers focused on Esophageal Cancer Research and Treatment (16 papers), Radiomics and Machine Learning in Medical Imaging (8 papers) and Epigenetics and DNA Methylation (6 papers). Xin Song collaborates with scholars based in China, United States and Hong Kong. Xin Song's co-authors include Ruilei Li, Yuanbo Xue, Xueke Zhao, Nan Chen, Qiaofen Fu, Gaofeng Li, Ying Wang, Zhen Li, Heng Li and Baozhen Zeng and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Xin Song

55 papers receiving 871 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Xin Song	425	401	206	165	141	57	879
Roger Mouawad	519 1.2×	307 0.8×	208 1.0×	177 1.1×	121 0.9×	57	963
Jianbo Zhang	324 0.8×	479 1.2×	203 1.0×	221 1.3×	205 1.5×	48	877
Natalie Y.L. Ngoi	419 1.0×	491 1.2×	130 0.6×	145 0.9×	89 0.6×	69	892
Vildan Yasasever	435 1.0×	458 1.1×	204 1.0×	195 1.2×	64 0.5×	53	956
D Glaves	346 0.8×	381 1.0×	228 1.1×	205 1.2×	152 1.1×	41	987
Aeree Kim	547 1.3×	442 1.1×	169 0.8×	263 1.6×	72 0.5×	41	1.0k
Robert P. Whitehead	283 0.7×	529 1.3×	176 0.9×	161 1.0×	82 0.6×	38	823
Matthew A. Smith	390 0.9×	349 0.9×	247 1.2×	139 0.8×	193 1.4×	37	1.1k
Łukasz Szylberg	310 0.7×	362 0.9×	113 0.5×	210 1.3×	154 1.1×	81	910
Jing Liu	415 1.0×	350 0.9×	236 1.1×	200 1.2×	110 0.8×	32	957

Countries citing papers authored by Xin Song

Since Specialization

Citations

This map shows the geographic impact of Xin Song's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xin Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xin Song more than expected).

Fields of papers citing papers by Xin Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xin Song. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xin Song. The network helps show where Xin Song may publish in the future.

Co-authorship network of co-authors of Xin Song

This figure shows the co-authorship network connecting the top 25 collaborators of Xin Song. A scholar is included among the top collaborators of Xin Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xin Song. Xin Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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